Quantifying Changes in the Language Used Around Mental Health on Twitter Over 10 Years: Observational Study

Anne Marie Stupinski; Thayer Alshaabi; Michael V Arnold; Jane Lydia Adams; Joshua R Minot; Matthew Price; Peter Sheridan Dodds; Christopher M Danforth

doi:10.2196/33685

Quantifying Changes in the Language Used Around Mental Health on Twitter Over 10 Years: Observational Study

JMIR Ment Health. 2022 Mar 30;9(3):e33685. doi: 10.2196/33685.

Authors

Anne Marie Stupinski¹, Thayer Alshaabi^{1

2}, Michael V Arnold¹, Jane Lydia Adams^{1

3}, Joshua R Minot¹, Matthew Price⁴, Peter Sheridan Dodds^{1

5}, Christopher M Danforth^{1

6}

Affiliations

¹ Computational Story Lab, Vermont Complex Systems Center, University of Vermont, Burlington, VT, United States.
² Advanced Bioimaging Center, University of California, Berkeley, CA, United States.
³ Data Visualization Lab, Khoury College of Computer Sciences, Northeastern University, Boston, MA, United States.
⁴ Department of Psychological Science, University of Vermont, Burlington, VT, United States.
⁵ Department of Computer Science, University of Vermont, Burlington, VT, United States.
⁶ Department of Mathematics and Statistics, University of Vermont, Burlington, VT, United States.

PMID: 35353049
PMCID: PMC9008521
DOI: 10.2196/33685

Abstract

Background: Mental health challenges are thought to affect approximately 10% of the global population each year, with many of those affected going untreated because of the stigma and limited access to services. As social media lowers the barrier for joining difficult conversations and finding supportive groups, Twitter is an open source of language data describing the changing experience of a stigmatized group.

Objective: By measuring changes in the conversation around mental health on Twitter, we aim to quantify the hypothesized increase in discussions and awareness of the topic as well as the corresponding reduction in stigma around mental health.

Methods: We explored trends in words and phrases related to mental health through a collection of 1-, 2-, and 3-grams parsed from a data stream of approximately 10% of all English tweets from 2010 to 2021. We examined temporal dynamics of mental health language and measured levels of positivity of the messages. Finally, we used the ratio of original tweets to retweets to quantify the fraction of appearances of mental health language that was due to social amplification.

Results: We found that the popularity of the phrase mental health increased by nearly two orders of magnitude between 2012 and 2018. We observed that mentions of mental health spiked annually and reliably because of mental health awareness campaigns as well as unpredictably in response to mass shootings, celebrities dying by suicide, and popular fictional television stories portraying suicide. We found that the level of positivity of messages containing mental health, while stable through the growth period, has declined recently. Finally, we observed that since 2015, mentions of mental health have become increasingly due to retweets, suggesting that the stigma associated with the discussion of mental health on Twitter has diminished with time.

Conclusions: These results provide useful texture regarding the growing conversation around mental health on Twitter and suggest that more awareness and acceptance has been brought to the topic compared with past years.

Keywords: mental health; natural language processing; stigma.

©Anne Marie Stupinski, Thayer Alshaabi, Michael V Arnold, Jane Lydia Adams, Joshua R Minot, Matthew Price, Peter Sheridan Dodds, Christopher M Danforth. Originally published in JMIR Mental Health (https://mental.jmir.org), 30.03.2022.